Understanding Global Dynamics: Population,

Health Systems, Gender Equality

Teammates:

1. Jaynica Nunna 11697960

2. Vitesh Chalicheemala 11689328

3. Mounika Vankayalapati 11714417

4. Supriya Ravilla 11644767

Introduction

Our project focuses on understanding crucial aspects of global

dynamics, including population trends, healthcare systems, and

gender equality.

By analyzing and visualizing data related to these domains, we aim to

uncover insights that contribute to informed decision-making and

drive positive societal change.

Through this endeavor, we seek to provide valuable insights into the

complex interplay of factors shaping our world today.

Data Abstraction

Dataset:

We utilized three datasets sourced from reputable sources such as the Kaggle and one from given ten

choice of datasets.

These datasets encompassed a wide range of attributes, including population demographics, healthcare

infrastructure, innovation indices, and gender inequality metrics.

Number of Records:

The combined datasets comprised thousands of records, with each record representing a specific

country or region.

The exact number varied across datasets but totaled well over a thousand records in aggregate.

Data Transformation:

Before analysis and visualization, we performed extensive data preprocessing to clean and harmonize

the datasets. This involved tasks such as handling missing values, standardizing units of measurement,

and ensuring data consistency across different sources.

Above Workflow Explaination

Data Collection:

Getting relevant datasets from Kaggle, a popular platform for data science datasets.

Initial Visualization:

Utilizing D3.js to visualize the uncleaned dataset, providing an initial overview of the data's structure and

potential insights.

Data Cleaning:

Using Python programming language and libraries like Pandas and NumPy to clean the dataset, handling

missing values, outliers, and inconsistencies.

Refined Visualization:

Using Python's data visualization libraries such as Matplotlib or Seaborn to create more refined visualizations

based on the cleaned dataset, focusing on specific variables of interest.

Dashboard Creation:

Utilizing Microsoft Power BI to design interactive dashboards and reports, incorporating the refined

visualizations to provide a comprehensive view of the analyzed data.

Report Generation:

Creating detailed reports summarizing the analysis findings, insights, and conclusions drawn from the

visualizations and data analysis process.

Task Abstraction

Task:

Target:

To analyze and visualize key aspects of global dynamics, including population demographics, healthcare

systems, and gender equality metrics.

Actions:

Data Collection: Gather relevant datasets from Kaggle, focusing on population demographics, healthcare

infrastructure, innovation indices, and gender inequality metrics.

Initial Visualization: Utilize D3.js to create preliminary visualizations of the uncleaned dataset, providing an

initial overview of its structure and potential insights.

Data Cleaning: Employ Python programming language and libraries like Pandas and NumPy to clean the

dataset, handling missing values, outliers, and inconsistencies.

Refined Visualization: Use Python's data visualization libraries such as Matplotlib or Seaborn to generate

more polished visualizations based on the cleaned dataset, highlighting specific variables of interest.

Dashboard Creation: Utilize Microsoft Power BI to design interactive dashboards and reports, integrating the

refined visualizations to offer a comprehensive view of the analyzed data.

Report Generation: Create detailed reports summarizing analysis findings, insights, and conclusions drawn

from the visualizations and data analysis process, facilitating informed decision-making.

Implementation using Tools:

D3.js:

Description: D3.js (Data-Driven Documents) was utilized for the initial visualization of the uncleaned dataset. It

provided a platform for creating dynamic and interactive charts and graphs directly within web browsers.

Usage: Through D3.js, we generated interactive visualizations such as bar charts, scatter plots, and heatmaps to

explore the structure and patterns within the raw dataset.

Python:

Description: Python, along with libraries like Pandas, NumPy, Matplotlib, and Seaborn, played a crucial role in data

preprocessing, analysis, and visualization.

Usage: Pandas and NumPy were employed for data cleaning tasks, including handling missing values, outliers, and

inconsistencies. Matplotlib and Seaborn were used to create refined visualizations based on the cleaned dataset,

showcasing insights through various types of charts and plots.

Microsoft Power BI:

Description: Microsoft Power BI served as a comprehensive platform for designing interactive dashboards and reports,

integrating visualizations to provide a comprehensive view of the analyzed data.

Usage: Power BI enabled us to create interactive visualizations, including bar charts, line graphs, and maps, and

seamlessly integrate them into interactive dashboards. Additionally, Power BI's data modeling capabilities allowed for

the creation of relationships between different datasets, enhancing the depth of analysis in the final reports.

Results for

Analysis

Using D3

This visualization represents the

population of the top ten countries

in 2023.

Each bar's height corresponds to the

population size of a specific country.

By comparing the heights of the

bars, we can see which countries

have larger populations relative to

others

And data used here is not

preprocessd or cleaned.

Using D3

we're visualizing the Global

Innovation Index (GII) of the top ten

countries.

Each bar represents a country, and

the height of the bar indicates its GII

score.

This visualization allows us to

compare the innovation levels of

different countries, with taller bars

indicating higher innovation scores.

And data used here is not

preprocessd or cleaned.

Data Pre-processing

using python

After preprocessing datasets, we

stored all three datasets in to csv.,

using pandas.

Later using Visualisation libraries

we created below visuals.

Relationship between

pysicians and birth

registration

In terms of healthcare systems, each

point in this scatter plot represents a

nation's journey towards wellness.

As we see the axis of physicians per 1000

people, we uncover a tale of access to

healthcare.

Meanwhile, the completeness of birth

registration whispers a story of

governance and care, painting a picture

of a nation's commitment to its citizens'

well-being.

On top we have shown same visual

using D3 , that’s before data

preprocessing.

Population health

heatmap

Across the world, this map paints a

picture of people's presence.

Each color showcases the density

of population, offering a

straightforward glimpse into where

people gather, from bustling cities

to quieter regions.

Gender inequality index

map

This map provides a straightforward

view of gender inequality across the

world.

Darker colors indicate higher levels

of inequality, while lighter shades

represent areas with less disparity

between genders.

It's a clear snapshot of the global

landscape of gender equality efforts.

Next we are going to

create more visual

insights using powerBI

by embedding report

here.

Microsoft Power BI

Work Management: Implementation Status

Report

Work Completed:

Data Collection:

Description: Gathered relevant datasets from Kaggle.

Responsibility: All team members contributed to identifying and selecting datasets.

Contributions: Equal contribution from all team members.

Initial Visualization (D3.js):

Description: Created preliminary visualizations of the uncleaned dataset using D3.js.

Responsibility: Vitesh Chalicheemala

Contributions: Vitesh contributed 100% to this task.

Data Cleaning (Python):

Description: Cleaned the dataset using Python, Pandas, and NumPy.

Responsibility: Jaynica Nunna, Mounika Vankayalapati

Contributions: Jaynica and Mounika each contributed 50% to this task.

Refined Visualization (Python):

Description: Created refined visualizations based on the cleaned dataset using Python's data visualization libraries.

Responsibility: Supriya Ravilla

Contributions: Supriya contributed 100% to this task.

Work Management: Implementation

Status Report

Dashboard Creation (Microsoft Power BI):

Description: Designed interactive dashboards and reports using Microsoft

Power BI.

Responsibility: All team members collaborated on dashboard design and

implementation.

Contributions: Equal contribution from all team members.

Report Generation:

Description: Created detailed reports summarizing analysis findings.

Responsibility: Jaynica Nunna

Contributions: Jaynica contributed 100% to this task.

Overall Contributions

Jaynica Nunna: 25%

Vitesh Chalicheemala: 25%

Mounika Vankayalapati: 25%

Supriya Ravilla: 25%

References

1. Kaggle. (n.d.). Kaggle: Your Home for Data Science. Retrieved from

https://www.kaggle.com/

2. D3.js. (n.d.). D3.js - Data-Driven Documents. Retrieved from

https://d3js.org/

3. Python Software Foundation. (n.d.). Python. Retrieved from

https://www.python.org/

4. McKinney, W., Perktold, J., Seabold, S., & Wes McKinney. (2020). pandas-

dev/pandas: Pandas 1.2.4. Zenodo. https://doi.org/10.5281/zenodo.4611042

5. Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in

Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55

6. Microsoft Corporation. (n.d.). Power BI. Retrieved from

https://powerbi.microsoft.com/